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DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗

DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗

DOE OSTI2018

ATLAS: Story Maps for Communicating Groundwater Cleanup Status – 18510

The U.S. Department of Energy (DOE) manages one of the largest groundwater cleanup efforts in the world in terms of the number of plumes, diversity of contaminants, volume of affected groundwater, and diversity of hydrogeologic settings. The DOE Office of Environmental Management (EM) has been collecting site-specific data on plume sizes, remedial approaches, exit strategies, long-term stewardship requirements, and regulatory drivers at all DOE sites, and summarizing them in a single document to communicate high-level information on groundwater contamination and cleanup status to DOE managers. Recently, EM has sponsored an effort to transfer the document-based information to an online resource for collating information on groundwater plumes that is easily accessible by interested parties. Specifically, Web GIS story maps have been developed to provide a robust geospatial visualization of plumes at DOE sites using an intuitive interface that allows users to explore plume maps, explanatory text, photographs, and video provided within the story maps. This collection of story maps is called ATLAS (Assembly of Tools for Learning Actions and Status), and not only communicates groundwater plume data for each individual site, but also summarizes pertinent metrics across all EM sites. With ATLAS, sites with similar settings, contamination, or remedial approaches can be easily identified, facilitating effective communication among DOE sites and sharing of individual successes and lessons learned.

Freedman, Vicky L.↗

DOE OSTI2023

Preliminary Results of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying the solvent extraction process and the use of centrifugal contactors. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, color, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data supports the development of safeguards by design, provides operator process awareness, and aids in the discovery of process anomalies. This paper highlights some of the preliminary results from initial data collection campaigns and shares some of the lessons learned.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2018

ATLAS: Story Maps for Communicating Groundwater Cleanup Status - 18510

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Eyes On the Ground: Year 2 Assessment.

The goal of the Eyes On the Ground project is to develop tools to aid IAEA inspectors. Our original vision was to produce a tool that would take three-dimensional measurements of an unknown piece of equipment, construct a semantic representation of the measured object, and then use the resulting data to infer possible explanations of equipment function. We report our tests of a 3-d laser scanner to obtain 3-d point cloud data, and subsequent tests of software to convert the resulting point clouds into primitive geometric objects such as planes and cylinders. These tests successfully identified pipes of moderate diameter and planar surfaces, but also incurred significant noise. We also investigated the IAEA inspector task context, and learned that task constraints may present significant obstacles to using 3-d laser scanners. We further learned that equipment scale and enclosing cases may confound our original goal of equipment diagnosis. Meanwhile, we also surveyed the rapidly evolving field of 3-d measurement technology, and identified alternative sensor modalities that may prove more suitable for inspector use in a safeguards context. We conclude with a detailed discussion of lessons learned and the resulting implications for project goals. Approved for public release; further dissemination unlimited.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Solar Pathways in Federal Energy Assistance Programs: Expanding Low Income Home Energy Assistance Program (LIHEAP) and Weatherization Assistance Program (WAP)

How can solar best fit within your LIHEAP or WAP activities? Come join NREL and learn about the various pathways and new resources available to help implement solar in low-income programs. Panelists will share results from a multi-year research project, including survey results on LIHEAP and WAP solar adoption across the United States. This session will highlight case studies from early implementers, key lessons learned and resources developed based on stakeholder feedback for interested organizations. Attendees can expect gain a better understanding of the perceived barriers and opportunities to solar implementation, including the importance of partner coordination and complementary funding sources, and the next steps for how to get started. Additionally, attendees will hear from a local implementer of solar in WAP about their program and process.

Colorado↗

DOE OSTI2015

Aspirations and common tensions: larger lessons from the third US national climate assessment

The Third US National Climate Assessment (NCA3) was produced by experts in response to the US Global Change Research Act of 1990. Based on lessons learned from previous domestic and international assessments, the NCA3 was designed to speak to a broad public and inform the concerns of policy- and decision-makers at different scales. The NCA3 was also intended to be the first step in an ongoing assessment process that would build the nation’s capacity to respond to climate change. This concluding paper draws larger lessons from the insights gained throughout the assessment process that are of significance to future US and international assessment designers. We bring attention to process and products delivered, communication and engagement efforts, and how they contributed to the sustained assessment. Based on areas where expectations were exceeded or not fully met, we address four common tensions that all assessment designers must confront and manage: between (1) core assessment ingredients (knowledge base, institutional set-up, principled process, and the people involved), (2) national scope and subnational adaptive management information needs, (3) scope, complexity, and manageability, and (4) deliberate evaluation and ongoing learning approaches. Managing these tensions, amidst the social and political contexts in which assessments are conducted, is critical to ensure that assessments are feasible and productive, while its outcomes are perceived as credible, salient, and legitimate.

Moser, Susanne C.↗

DOE OSTI2024

Board 160: Empowerment in STEM Day: Introducing High School Girls to Careers at National Laboratories (Work in Progress)

In the US, women are still vastly underrepresented in STEM (science, technology, engineering, and mathematics) careers, and various studies have shown that girls' interest in STEM careers wane as high school progresses. With this challenge in mind, Empowerment in STEM Day was organized by Lawrence Berkeley National Laboratory (LBNL), hosting 47 high school students from 6 public high schools in the area. This one-day event was designed and executed through a collaboration between the Women's Support and Empowerment Council (WSEC) and the K-12 STEM Education and Outreach Program at LBNL. The main goal of this program was to provide high school girls, who have little access to STEM career role-models in their immediate surroundings with insights into how a career in STEM looks like. Invitations to participate in the program were sent out to six local high schools in the Bay area asking educators to identify female students that were interested in STEM. Each high school participant was provided with an opportunity to experience a national laboratory environment, learn more about summer workshops and paid summer research internship opportunities at LBNL for high school students, and engage directly with LBNL's employees through job shadow, career mapping and speed networking sessions. In this paper, we will present an overview of the event organization, challenges faced during planning and execution of the event, discuss the lessons learned from the first Empowerment in STEM Day and suggest strategies for incorporating such events at other national laboratories and academic institutions as part of a vital effort into recruiting and retaining more high school girls in STEM-based careers. Additionally, since this was the first in-person event hosted by LBNL's K-12 Program after the pandemic, we will also share the strategies implemented at the event so as to engage both remote and on-site employees as volunteers.

Bose, Baishakhi↗

DOE OSTI2026

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

42 ENGINEERING↗

DOE OSTI2018

Summary of Observations from the 2017 Federal Radiological Monitoring and Assessment Center (FRMAC) Laboratory Analysis Training and Capstone Event

On August 15th thru 17th, 2017 the Federal Radiological Monitoring and Assessment Center (FRMAC) Laboratory Analysis division, the FRMAC Fly Away Laboratory (FAL), the FRMAC Assessment division, and the Mobile Environmental Response Laboratory (MERL) held a training and capstone event for staff from the Environmental Protection Agency (EPA), Remote Sensing Laboratory (RSL), Lawrence Livermore National Laboratory (LLNL), and Sandia National Laboratories (SNL). LAB-100, "Sample Control Training", LAB-200 "QA Specialist Training", and LAB-300 "Laboratory Analysis Manager Training" was given the first two days of the event. The purpose of the training and capstone event was to meet training requirements for billeted FRMAC Lab Analysis staff from RSL, LLNL, and SNL as well as raise awareness of the FRMAC Lab Analysis process with EPA staff as part of a Federal Emergency Management Agency — Nuclear Incident Response Team (FEMA-NIRT) laboratory standardization project. An objective of the standardization project was to help improve the transition of operations from DOE to EPA during a response. To do this effectively, detailed knowledge of the FRMAC Lab Analysis process by the EPA is needed. This training provided a good opportunity for this knowledge transfer. A capstone was held after the two-day training event to allow participants to practice the skills they learned in a realistic scenario. A scenario that was previously developed for a quarterly Consequence Management drill (i.e. Dark Phoenix) was used as the basis for the capstone, with laboratory analysis focused injects used to drive the exercise play. Each position within the FRMAC Lab Analysis Division exercised to specific objectives and helped to uncover gaps in the established processes. The lessons learned during this capstone are broken out in the following categories: Sample Control, In-Situ Gamma Spectroscopy, Analysis Request Forms (ARF), Shipping, QA/QC, Fly Away Laboratory (FAL), and Management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Accelerating Science with the NERSC Burst Buffer Early User Program

NVRAM-based Burst Buffers are an important part of the emerging HPC storage landscape. The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory recently installed one of the first Burst Buffer systems as part of its new Cori supercomputer, collaborating with Cray on the development of the DataWarp software. NERSC has a diverse user base comprised of over 6500 users in 700 different projects spanning a wide variety of scientific computing applications. The use-cases of the Burst Buffer at NERSC are therefore also considerable and diverse. We describe here performance measurements and lessons learned from the Burst Buffer Early User Program at NERSC, which selected a number of research projects to gain early access to the Burst Buffer and exercise its capability to enable new scientific advancements. To the best of our knowledge this is the first time a Burst Buffer has been stressed at scale by diverse, real user workloads and therefore these lessons will be of considerable benefit to shaping the developing use of Burst Buffers at HPC centers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

What can we learn from decarbonization of the energy sector?

This is a chapter that will be part of an Open Source e-book published by the International Water Association Publishing House. The co-editors are Professors Z. Jason Ren and Krishna Pagilla. Decarbonizing water and wastewater treatment is an enormous challenge, but it is substantially smaller, in total carbon emissions, than decarbonizing the energy sector. When planning, executing, and assessing strategies for decarbonizing the water sector, water experts should partner with the energy sector and heed that sector’s lessons-learned in its ongoing process of decarbonization. In the energy sector, decarbonization pathways can be as simple as a supply-side technology that converts fuel to electricity more efficiently, reducing net carbon emissions for every kilowatt-hour generated. The pathways can be much more complex, however, as is the case with the demand-side reordering of behavior as seen with online shopping or working from home during a public health crisis. Both of those pathways reduce demand for private-vehicle fuel and shift some work, and associated carbon emissions, to other parts of the economy. This chapter explores decarbonization pathways that have been followed by the energy sector and assesses their applicability to the water sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Evaluation of Factors that Influence Residential Solar Panel Installations

Though rooftop photovoltaic (PV) systems are the fastest growing source of distributed generation, detailed information about where they are located and who their owners are is often known only to installers and utility companies. This lack of detailed information is a barrier to policy and financial assessment of solar energy generation and use. To bridge the described data gap, Oak Ridge National Laboratory (ORNL) was sponsored by the Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA) to create an automated approach for detecting and characterizing buildings with installed solar panels using high-resolution overhead imagery. Additionally, ORNL was tasked with using machine learning techniques to classify parcels on which solar panels were automatically detected in the Washington, DC, and Boston areas as commercial or residential, and then providing a list of recommended variables and modeling techniques that could be combined with these results to identify attributes that motivate the installation of residential solar panels. This technical report describes the methodology, results, and recommendations in greater detail, including lessons learned and future work.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

Dealing with Historical Discrepancies: The Recovery of National Research Experiment (NRX) Reactor Fuel Rods at Chalk River Laboratories (CRL) - 13324

Following the 1952 National Research Experiment (NRX) Reactor accident, fuel rods which had short irradiation histories were 'temporarily' buried in wooden boxes at the 'disposal grounds' during the cleanup effort. The Nuclear Legacy Liabilities Program (NLLP), funded by Natural Resources Canada (NRCan), strategically retrieves legacy waste and restores lands affected by Atomic Energy of Canada Limited (AECL) early operations. Thus under this program the recovery of still buried NRX reactor fuel rods and their relocation to modern fuel storage was identified as a priority. A suspect inventory of NRX fuels was compiled from historical records and various research activities. Site characterization in 2005 verified the physical location of the fuel rods and determined the wooden boxes they were buried in had degraded such that the fuel rods were in direct contact with the soil. The fuel rods were recovered and transferred to a modern fuel storage facility in 2007. Recovered identification tags and measured radiation fields were used to identify the inventory of these fuels. During the retrieval activity, a discrepancy was discovered between the anticipated number of fuel rods and the number found during the retrieval. A total of 32 fuel rods and cans of cut end pieces were recovered from the specified site, which was greater than the anticipated 19 fuel rods and cans. This discovery delayed the completion of the project, increased the associated costs, and required more than anticipated storage space in the modern fuel storage facility. A number of lessons learned were identified following completion of this project, the most significant of which was the potential for discrepancies within the historical records. Historical discrepancies are more likely to be resolved by comprehensive historical record searches and site characterizations. It was also recommended that a complete review of the wastes generated, and the total affected lands as a result of this historic 1952 NRX accident be undertaken. These lessons and recommendations have lead to changes in how the NLLP is executed in the CRL waste management areas. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Technical Performance and Cost Optimization of Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) for Wide-area Surveillance and Identification of Marine Life at Marine Energy Installations

Florida Atlantic University developed an underwater optical monitoring system prototype - Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) - suitable for marine energy full project lifecycle observation (baseline, commissioning, and decommissioning), with an automated real-time classification of marine animals. With precursor 2014 DOE funding (award DE-EE0006787), a prototype UMSLI was demonstrated in a controlled laboratory environment and achieved a TRL 6. This EERE DE-EE0007828 award aimed to both increase the TRL of the UMSLI by improving the technology performance (e.g., increase distance of marine animal target detection capability, add additional species classification capabilities, improve the system performance during the day, etc.) and reduce the cost. The UMSLI presents a novel application of underwater distributed Light Detection And Ranging (LiDAR), an advanced remote sensing method that uses light in the form of laser pulses for an application, this paired with an algorithm provides 360 degrees underwater detection, imaging, and classification of marine life. This solution for underwater monitoring of biota preserves the advantages of traditional optical and acoustic solutions while overcoming many associated disadvantages for marine energy site environmental monitoring, such as difficulties in species detection and classification in low light or night and turbid environments. This new approach is a purposefully designed, reconfigurable adaptation of an existing class 3B laser technology into one UMSLI instrument that can be easily mounted on or around different classes of marine energy equipment, such as devices to capture ocean current energy or wave energy. The system uses relatively low average power, utilizes far-red (> 635nm) laser illumination to be invisible and eye-safe to marine animals, is compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations (i.e., current design is targeting more than 7 days continuous operation), to inherently generate a sparse primary dataset that only includes detected anomalies, and to allow robust real-time automated animal classification and identification with a low data bandwidth requirement. The technology’s overarching goal for application, is a system that can be deployed to collect pre-installation baseline species observations at a proposed marine energy deployment site with minimal post-processing overhead. The envisioned system will also produce high-resolution imagery of marine animals through a wide range of conditions and support automated tracking and notification of the presence of managed animals within established perimeters of marine energy equipment to satisfy deployed marine energy projects’ endangered and threatened species monitoring requirements. Through the current project, we demonstrated the UMSLI prototype in an operational environment and increased the UMSLI’s Technology Readiness Level from 6 to 7. The project resulted in many novel technologies, including: 1) an eye-safe red laser based, low-cost LiDAR system that can detect targets up to 10 meters distance; 2) the GAN-based machine learning underwater LiDAR image enhancement technique (this is the first known application of GAN technique in underwater LiDAR); 3) LiDAR-based real-time automated detection capabilities; and 4) a template matching based automated classification tool. These technologies build a solid foundation for future efforts to develop an extended range electro-optical monitoring system suitable for marine energy deployments. In addition to technology development, a key lesson learned is that addressing regulatory and safety requirements must be front and center in any marine energy monitoring applications.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

Reliable and Durable Communication: Consistently Connecting DuraMAT Messages with Target Audiences

This webinar addresses communications strategies for the Durable Module Materials Consortium (DuraMAT). DuraMAT focuses on precompetitive research needs in solar photovoltaic (PV) module packaging to accelerate a sustainable, just, and equitable transition to zero carbon electricity generation by 2035. In the webinar, an interactive exercise helps researchers write better article titles. DuraMAT communications goals and lessons learned are discussed. Key audiences for DuraMAT communication products are identified. Next steps for DuraMAT communications are summarized.

audiences↗

DOE OSTI2019

Target Control and Protection Systems Lessons from SNS Operations

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory has been in operations since 2006 and proposes a project to build a Second Target Station (STS) to effectively double potential scientific output. The SNS target controls operate in a harsh environment which includes high radiation, exposure to gaseous radionuclides, and activated liquid mercury and mercury vapor. These conditions necessitate protective interlocks and credited controls for protection functions to ensure proper response to off-normal conditions. In order to inform the design of target controls for the STS, we have examined lessons learned during SNS operations regarding the design and implementation of the control and protection systems for the first target station (FTS). This paper will examine various aspects of the performance of the target control and protection systems including reliability, maintainability and sustainability given the challenging environment created by 1.4 MW operations. Specific topics include distributed control of various target subsystems, response to loss of power, selection of nuclear grade instrumentation, and applying these lessons to the design for the STS project.

Humphreys, Dana↗